Decorative 3D background: a mission control room. Tiered rows of consoles face a front wall carrying three projection boards — a ground track marking the cities where he has studied and worked, a strip chart of repositories created per year by domain, and a GO / STAND BY status board. Scrolling moves the viewpoint down the aisle toward the wall. Every value shown on the boards is also written out in the page text below.
Mansurbek Satarov
Trustworthy AI ResearcherML Systems EngineerFull-Stack Developer
Ph.D. researcher building trustworthy AI systems, studying how model behavior emerges during training, and engineering production software across machine learning, computer vision, agentic AI, cloud infrastructure, and interactive web applications.
Open to research collaborations and internships
The poll
Before a burn, a flight director calls every station and waits for an answer. This site answers the same way about its own evidence: a station reports GO only where a public, machine-checkable artifact backs the claim.
Flight: all stations, go / no-go for evaluation
Polling…
- Systems———20 of 22 case studies resolve to a public repository
- Network———45 repositories classified as own work from commit data
- Sim Bay———Five simulations, each with a pure core under unit test
- Science———Research direction stated; individual work not yet citable
- Declared———2 projects are résumé-declared with no public repository
Stand by is not a failure state — it marks work declared on a résumé that no public repository can confirm. It is on this board so you know exactly which claims you can check yourself.
Systems
All 22 systems →Research systems, agentic pipelines and production ML, each with the architecture that made it work and the results it actually produced.
Flight data file — selected systems
- 001
Multi-Agent Deep Research
A LangGraph supervisor/worker research workflow that plans, gathers evidence, extracts structured findings, and writes sourced reports.
Python · LangGraph · OpenAI API
Complete - 002
Retail Intelligence Platform
End-to-end Azure retail analytics: customer lifetime value, churn risk, basket associations, household search, and cached dashboards.
FastAPI · React · Vite
Complete - 003
RAG Assistant
A modular retrieval-augmented generation pipeline: ingestion, chunking, embeddings, ChromaDB vector search, and a conversational interface.
Python · Flask · ChromaDB
Complete - 004
DinoMind Evolution
NEAT neuroevolution learns to play a dino runner — neural networks evolve generation by generation to dodge obstacles.
Python · Pygame · NEAT-Python
Complete - 005
DocuParse
High-performance PDF/EPUB/MOBI → structured Markdown conversion with AI layout detection, LaTeX equation handling, and multilingual support.
Python · OCR · Layout-detection models
Complete - 006
DigitRecognizer
A neural network handwritten from scratch in NumPy — forward propagation, backprop, and gradient descent on MNIST, no frameworks.
Python · NumPy · Matplotlib
Complete
Science
Enter the science station →Ph.D. research on how model behavior forms during training, and how to keep it predictable once it has.
Channel select
Channel readout
Trustworthy machine learning
Making model behavior predictable, auditable, and safe to depend on.
1 / 10 channels
Network
Acquire the network →Every public repository, labeled by a relationship computed from commit data. Forks and stars are listed as exploration and reading — they are never counted as contribution.
- Own work
- 45
- Forks kept for study
- 30
- Saved references
- 68
- Technologies used
- 59
Own work by domain
- Web20
- Data science7
- Computer vision6
- LLM & agents4
- Systems2
- Cloud2
- Games2
- Other1
- Machine learning1
Bars count repositories the classifier resolved to his own commit history. Forks and stars are excluded from every bar on this panel.
Label definitions
- Built by me
- Owned, not a fork, with a substantial own-commit history.
- Co-built
- Owned, with two or more active co-contributors.
- Contributed
- Someone else's repository, with merged pull requests.
- Forked & explored
- A fork kept for study. Counts as reading, not work.
- Studied / used
- Depended on or read closely, without commits.
- Saved resource
- Starred for reference. Zero contribution claimed.
Sim bay
Enter the sim bay →Five simulations that run in the browser. Each has a pure, deterministic core under unit test — the same code the tests replay is the code you play.
SIM-01
DinoMind Arena
Race a visualized AI runner over the identical seeded course.
Space or ↑ to jump · ↓ to duck · P to pause
SIM-02
Re-Entry Corridor
Hold the descent inside the corridor on a fixed propellant budget.
↑ retro burn · ← → adjust attitude · P to pause
SIM-03
Go / No-Go Drill
Read four gauges and call the burn before the clock runs out.
G to call GO · N to call NO-GO · or use the buttons
SIM-04
Neural Tic-Tac-Toe
Exact alpha-beta on 3×3, depth-limited search on 3×3×3.
Click or Tab + Enter · every square is a button
SIM-05
Typing Challenge
Machine-learning terms and real code, scored on live WPM and accuracy.
Just type · Esc to reset
Capcom
The only station that talks back. Open to research collaboration, Ph.D.-adjacent work, and engineering that has to hold up.